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Machine Learning of Procedural Audio


Game Audio

Project proposed & supervised by

Joshua Reiss

To discuss whether this project could become your PhD proposal please email:

Machine Learning of Procedural Audio

Project proposal abstract:

Game sound design relies heavily on pre-recorded samples, but this approach is inflexible, repetitive and uncreative. An alternative is procedural audio, where sounds are created in real-time using software algorithms. But many procedural audio techniques are low quality, or tailored only to a narrow class of sounds. Machine learning from sample libraries to select, optimise and improve the procedural models, could be the key to transforming the industry and creating procedural auditory worlds. This work will build on recent high impact research from the team to investigate whether procedural audio can fully replace the use of pre-recorded sound effects. See for examples of procedural sound effects.

Based at:

This project will be a collaboration with Nemesindo.

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